Fully probabilistic control for stochastic nonlinear control systems with input dependent noise

Robust controllers for nonlinear stochastic systems with functional uncertainties can be consistently designed using probabilistic control methods. In this paper a generalised probabilistic controller design for the minimisation of the Kullback-Leibler divergence between the actual joint probability density function (pdf) of the closed loop control system, and an ideal joint pdf is presented emphasising how the uncertainty can be systematically incorporated in the absence of reliable systems models. To achieve this objective all probabilistic models of the system are estimated from process data using mixture density networks (MDNs) where all the parameters of the estimated pdfs are taken to be state and control input dependent. Based on this dependency of the density parameters on the input values, explicit formulations to the construction of optimal generalised probabilistic controllers are obtained through the techniques of dynamic programming and adaptive critic methods. Using the proposed generalised probabilistic controller, the conditional joint pdfs can be made to follow the ideal ones. A simulation example is used to demonstrate the implementation of the algorithm and encouraging results are obtained.

A mixture density network approach to modelling and exploiting uncertainty in nonlinear control problems

In nonlinear and stochastic control problems, learning an efficient feed-forward controller is not amenable to conventional neurocontrol methods. For these approaches, estimating and then incorporating uncertainty in the controller and feed-forward models can produce more robust control results. Here, we introduce a novel inversion-based neurocontroller for solving control problems involving uncertain nonlinear systems which could also compensate for multi-valued systems. The approach uses recent developments in neural networks, especially in the context of modelling statistical distributions, which are applied to forward and inverse plant models. Provided that certain conditions are met, an estimate of the intrinsic uncertainty for the outputs of neural networks can be obtained using the statistical properties of networks. More generally, multicomponent distributions can be modelled by the mixture density network. Based on importance sampling from these distributions a novel robust inverse control approach is obtained. This importance sampling provides a structured and principled approach to constrain the complexity of the search space for the ideal control law. The developed methodology circumvents the dynamic programming problem by using the predicted neural network uncertainty to localise the possible control solutions to consider. A nonlinear multi-variable system with different delays between the input-output pairs is used to demonstrate the successful application of the developed control algorithm. The proposed method is suitable for redundant control systems and allows us to model strongly non-Gaussian distributions of control signal as well as processes with hysteresis. © 2004 Elsevier Ltd. All rights reserved.

Conditional distribution modeling for estimating and exploiting uncertainty in control systems

This paper presents a general methodology for estimating and incorporating uncertainty in the controller and forward models for noisy nonlinear control problems. Conditional distribution modeling in a neural network context is used to estimate uncertainty around the prediction of neural network outputs. The developed methodology circumvents the dynamic programming problem by using the predicted neural network uncertainty to localize the possible control solutions to consider. A nonlinear multivariable system with different delays between the input-output pairs is used to demonstrate the successful application of the developed control algorithm. The proposed method is suitable for redundant control systems and allows us to model strongly non Gaussian distributions of control signal as well as processes with hysteresis.

When do firms conform? The effect of regulatory control processes on compliance and opportunism

The authors use social control theory to develop a conceptual model that addresses the effectiveness of regulatory agencies’ (e.g., Food and Drug Administration, Occupational Safety and Health Administration) field-level efforts to obtain conformance with product safety laws. Central to the model are the control processes agencies use when monitoring organizations and enforcing the safety rules. These approaches can be labeled formal control (e.g., rigid enforcement) and informal control (e.g., social instruction). The theoretical framework identifies an important antecedent of control and the relative effectiveness of control’s alternative forms in gaining compliance and reducing opportunism. Furthermore, the model predicts that the regulated firms’ level of agreement with the safety rules moderates the relationships between control and firm responses. A local health department’s administration of state food safety regulations provides the empirical context for testing the hypotheses. The results from a survey of 173 restaurants largely support the proposed model. The study findings inform a discussion of effective methods of administering product safety laws. The authors use social control theory to develop a conceptual model that addresses the effectiveness of regulatory agencies’ (e.g., Food and Drug Administration, Occupational Safety and Health Administration) field-level efforts to obtain conformance with product safety laws. Central to the model are the control processes agencies use when monitoring organizations and enforcing the safety rules. These approaches can be labeled formal control (e.g., rigid enforcement) and informal control (e.g., social instruction). The theoretical framework identifies an important antecedent of control and the relative effectiveness of control’s alternative forms in gaining compliance and reducing opportunism. Furthermore, the model predicts that the regulated firms’ level of agreement with the safety rules moderates the relationships between control and firm responses. A local health department’s administration of state food safety regulations provides the empirical context for testing the hypotheses. The results from a survey of 173 restaurants largely support the proposed model. The study findings inform a discussion of effective methods of administering product safety laws.

Mathematical modelling and optimal multivariable control of chemical processes

This work reports the developnent of a mathenatical model and distributed, multi variable computer-control for a pilot plant double-effect climbing-film evaporator. A distributed-parameter model of the plant has been developed and the time-domain model transformed into the Laplace domain. The model has been further transformed into an integral domain conforming to an algebraic ring of polynomials, to eliminate the transcendental terms which arise in the Laplace domain due to the distributed nature of the plant model. This has made possible the application of linear control theories to a set of linear-partial differential equations. The models obtained have well tracked the experimental results of the plant. A distributed-computer network has been interfaced with the plant to implement digital controllers in a hierarchical structure. A modern rnultivariable Wiener-Hopf controller has been applled to the plant model. The application has revealed a limitation condition that the plant matrix should be positive-definite along the infinite frequency axis. A new multi variable control theory has emerged fram this study, which avoids the above limitation. The controller has the structure of the modern Wiener-Hopf controller, but with a unique feature enabling a designer to specify the closed-loop poles in advance and to shape the sensitivity matrix as required. In this way, the method treats directly the interaction problems found in the chemical processes with good tracking and regulation performances. Though the ability of the analytical design methods to determine once and for all whether a given set of specifications can be met is one of its chief advantages over the conventional trial-and-error design procedures. However, one disadvantage that offsets to some degree the enormous advantages is the relatively complicated algebra that must be employed in working out all but the simplest problem. Mathematical algorithms and computer software have been developed to treat some of the mathematical operations defined over the integral domain, such as matrix fraction description, spectral factorization, the Bezout identity, and the general manipulation of polynomial matrices. Hence, the design problems of Wiener-Hopf type of controllers and other similar algebraic design methods can be easily solved.

Nonlinear diffraction in sub-critical femtosecond inscription

Material processing using high-intensity femtosecond (fs) laser pulses is a fast developing technology holding potential for direct writing of multi-dimensional optical structures in transparent media. In this work we re-examine nonlinear diffraction theory in context of fs laser processing of silica in sub-critical (input power less than the critical power of self-focusing) regime. We have applied well known theory, developed by Vlasov, Petrishev and Talanov, that gives analytical description of the evolution of a root-mean-square beam (not necessarily Gaussian) width RRMS(z) in medium with the Kerr nonlinearity.

Design of a stock control methodology for use within an oil blending company

The thesis deals with the background, development and description of a mathematical stock control methodology for use within an oil and chemical blending company, where demand and replenishment lead-times are generally non-stationary. The stock control model proper relies on, as input, adaptive forecasts of demand determined for an economical forecast/replenishment period precalculated on an individual stock-item basis. The control procedure is principally that of the continuous review, reorder level type, where the reorder level and reorder quantity 'float', that is, each changes in accordance with changes in demand. Two versions of the Methodology are presented; a cost minimisation version and a service level version. Realising the importance of demand forecasts, four recognised variations of the Trigg and Leach adaptive forecasting routine are examined. A fifth variation, developed, is proposed as part of the stock control methodology. The results of testing the cost minimisation version of the Methodology with historical data, by means of a computerised simulation, are presented together with a description of the simulation used. The performance of the Methodology is in addition compared favourably to a rule-of-thumb approach considered by the Company as an interim solution for reducing stack levels. The contribution of the work to the field of scientific stock control is felt to be significant for the following reasons:- (I) The Methodology is designed specifically for use with non-stationary demand and for this reason alone appears to be unique. (2) The Methodology is unique in its approach and the cost-minimisation version is shown to work successfully with the demand data presented. (3) The Methodology and the thesis as a whole fill an important gap between complex mathematical stock control theory and practical application. A brief description of a computerised order processing/stock monitoring system, designed and implemented as a pre-requisite for the Methodology's practical operation, is presented as an appendix.

On the theory of self-similar parabolic optical pulses

Self-similar optical pulses (or “similaritons”) of parabolic intensity profile can be found as asymptotic solutions of the nonlinear Schr¨odinger equation in a gain medium such as a fiber amplifier or laser resonator. These solutions represent a wide-ranging significance example of dissipative nonlinear structures in optics. Here, we address some issues related to the formation and evolution of parabolic pulses in a fiber gain medium by means of semi-analytic approaches. In particular, the effect of the third-order dispersion on the structure of the asymptotic solution is examined. Our analysis is based on the resolution of ordinary differential equations, which enable us to describe the main properties of the pulse propagation and structural characteristics observable through direct numerical simulations of the basic partial differential equation model with sufficient accuracy.

On the theory of self-similar parabolic optical pulses

Self-similar optical pulses (or “similaritons”) of parabolic intensity profile can be found as asymptotic solutions of the nonlinear Schr¨odinger equation in a gain medium such as a fiber amplifier or laser resonator. These solutions represent a wide-ranging significance example of dissipative nonlinear structures in optics. Here, we address some issues related to the formation and evolution of parabolic pulses in a fiber gain medium by means of semi-analytic approaches. In particular, the effect of the third-order dispersion on the structure of the asymptotic solution is examined. Our analysis is based on the resolution of ordinary differential equations, which enable us to describe the main properties of the pulse propagation and structural characteristics observable through direct numerical simulations of the basic partial differential equation model with sufficient accuracy.

Nonlinear diffraction in sub-critical femtosecond inscription

Material processing using high-intensity femtosecond (fs) laser pulses is a fast developing technology holding potential for direct writing of multi-dimensional optical structures in transparent media. In this work we re-examine nonlinear diffraction theory in context of fs laser processing of silica in sub-critical (input power less than the critical power of self-focusing) regime. We have applied well known theory, developed by Vlasov, Petrishev and Talanov, that gives analytical description of the evolution of a root-mean-square beam (not necessarily Gaussian) width RRMS(z) in medium with the Kerr nonlinearity.

Fully probabilistic control design in an adaptive critic framework

Optimal stochastic controller pushes the closed-loop behavior as close as possible to the desired one. The fully probabilistic design (FPD) uses probabilistic description of the desired closed loop and minimizes Kullback-Leibler divergence of the closed-loop description to the desired one. Practical exploitation of the fully probabilistic design control theory continues to be hindered by the computational complexities involved in numerically solving the associated stochastic dynamic programming problem. In particular very hard multivariate integration and an approximate interpolation of the involved multivariate functions. This paper proposes a new fully probabilistic contro algorithm that uses the adaptive critic methods to circumvent the need for explicitly evaluating the optimal value function, thereby dramatically reducing computational requirements. This is a main contribution of this short paper.

Control of cascaded DC-DC converter based hybrid battery energy storage systems - Part II:Lyapunov approach

A cascaded DC-DC boost converter is one of the ways to integrate hybrid battery types within a grid-tie inverter. Due to the presence of different battery parameters within the system such as, state-of-charge and/or capacity, a module based distributed power sharing strategy may be used. To implement this sharing strategy, the desired control reference for each module voltage/current control loop needs to be dynamically varied according to these battery parameters. This can cause stability problem within the cascaded converters due to relative battery parameter variations when using the conventional PI control approach. This paper proposes a new control method based on Lyapunov Functions to eliminate this issue. The proposed solution provides a global asymptotic stability at a module level avoiding any instability issue due to parameter variations. A detailed analysis and design of the nonlinear control structure are presented under the distributed sharing control. At last thorough experimental investigations are shown to prove the effectiveness of the proposed control under grid-tie conditions.

Sub-critical regime of femtosecond inscription

We apply well known nonlinear diffraction theory governing focusing of a powerful light beam of arbitrary shape in medium with Kerr nonlinearity to the analysis of femtosecond (fs) laser processing of dielectric in sub-critical (input power less than the critical power of selffocusing) regime. Simple analytical expressions are derived for the input beam power and spatial focusing parameter (numerical aperture) that are required for achieving an inscription threshold. Application of non-Gaussian laser beams for better controlled fs inscription at higher powers is also discussed. © 2007 Optical Society of America.